Prior knowledge structured as a knowledge graph can improve the performance on image classification [1]}.
The Graph Search Neural Network (GSNN) [1]} incorporates prior knowledge into Graph Neural Networks (GNN) [3]} to solve a multi-task vision classification problem (see Tab. REF ). GSNN is based on the Gated Graph Neural Network (GGNN) [4]}, reducing computational requirements and observing the flow of information through the propagation model. GGNN uses the Gated Recurrent Unit (GRU) [5]} to update the hidden state of each node with information from the neighbouring nodes.
GGNN is a differential recurrent neural network that operates on graph data representations, iteratively propagating the relationships to learn node-level and graph-level representations.
The Graph Reasoning Model (GRM) [6]} uses objects and interactions between people to predict social relationships.
The graph model weighs the predicted relationships with a graph attention mechanism based on Graph Attention Networks (GAT) [7]}.
GRM uses prior knowledge on social relationships, co-occurrences and objects in the scene as a structured graph.
Interactions between people of interest and contextual objects are modelled by the GGNN [4]} model, where the nodes are initialised with the corresponding semantic regions.
The model learns about relevant objects that carry relevant task-information.
